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Most learning-based super-resolution (SR) methods aim to recoverhigh-resolution (HR) image from a given low-resolution (LR) image via learningon LR-HR image pairs. The SR methods learned on synthetic data do not performwell in real-world, due to the domain gap between the artificially synthesizedand real LR images. Some efforts are thus taken to capture real-world imagepairs. The captured LR-HR image pairs usually suffer from unavoidablemisalignment, which hampers the performance of end-to-end learning, however.Here, focusing on the real-world SR, we ask a different question: sincemisalignment is unavoidable, can we propose a method that does not need LR-HRimage pairing and alignment at all and utilize real images as they are? Hencewe propose a framework to learn SR from an arbitrary set of unpaired LR and HRimages and see how far a step can go in such a realistic and "unsupervised"setting. To do so, we firstly train a degradation generation network togenerate realistic LR images and, more importantly, to capture theirdistribution (i.e., learning to zoom out). Instead of assuming the domain gaphas been eliminated, we minimize the discrepancy between the generated data andreal data while learning a degradation adaptive SR network (i.e., learning tozoom in). The proposed unpaired method achieves state-of-the-art SR results onreal-world images, even in the datasets that favor the paired-learning methodsmore.

Learning to Zoom-in via Learning to Zoom-out: Real-world Super-resolution by Generating and Adapting Degradation